{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from deepsky.data import load_storm_patch_data\n",
    "from deepsky.gan import normalize_multivariate_data, stack_gen_disc, stack_enc_gen, train_gan_quiet, stack_gen_enc\n",
    "from deepsky.models import LogisticGAN\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Conv2D, Conv2DTranspose, Flatten, Dense, Input, UpSampling2D, MaxPool2D, BatchNormalization\n",
    "from keras.layers import Activation, Reshape, LeakyReLU, concatenate, Dropout, GaussianNoise, AveragePooling2D\n",
    "from keras.regularizers import l2\n",
    "from keras.optimizers import Adam\n",
    "import keras.backend as K\n",
    "import xarray as xr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/users/dgagne/ncar_ens_storm_patches/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                                                                             (p: 152, x: 64, y: 64)\n",
       "Coordinates:\n",
       "    longitude                                                                                           (p, y, x) float32 ...\n",
       "    latitude                                                                                            (p, y, x) float32 ...\n",
       "  * p                                                                                                   (p) uint32 0 ...\n",
       "  * y                                                                                                   (y) float32 0.0 ...\n",
       "  * x                                                                                                   (x) float32 0.0 ...\n",
       "Data variables:\n",
       "    row                                                                                                 (p, y, x) int32 ...\n",
       "    column                                                                                              (p, y, x) int32 ...\n",
       "    forecast_hour                                                                                       (p) datetime64[ns] ...\n",
       "    valid_date                                                                                          (p) datetime64[ns] ...\n",
       "    run_date                                                                                            (p) datetime64[ns] ...\n",
       "    mask                                                                                                (p, y, x) uint8 ...\n",
       "    hourly_maximum_of_upward_vertical_velocity_in_the_lowest_400hpa_400-1000_mb_above_ground            (p, y, x) float32 ...\n",
       "    composite_reflectivity_entire_atmosphere_current                                                    (p, y, x) float32 ...\n",
       "    reflectivity_263_k_level_current                                                                    (p, y, x) float32 ...\n",
       "    wind_speed_(gust)_surface_current                                                                   (p, y, x) float32 ...\n",
       "    hourly_maximum_of_downward_vertical_velocity_in_the_lowest_400hpa_400-1000_mb_above_ground_current  (p, y, x) float32 ...\n",
       "    reflectivity_4000_m_above_ground_current                                                            (p, y, x) float32 ...\n",
       "    reflectivity_1000_m_above_ground_current                                                            (p, y, x) float32 ...\n",
       "    hourly_maximum_of_simulated_reflectivity_at_1_km_agl_1000_m_above_ground_current                    (p, y, x) float32 ...\n",
       "    hourly_maximum_of_updraft_helicity_over_layer_2km_to_5_km_agl_5000-2000_m_above_ground_current      (p, y, x) float32 ...\n",
       "    total_column_integrate_graupel_entire_atmosphere_(considered_as_a_single_layer)_current             (p, y, x) float32 ...\n",
       "    geopotential_height_surface_current                                                                 (p, y, x) float32 ...\n",
       "    wind_speed_10_m_above_ground_current                                                                (p, y, x) float32 ...\n",
       "    total_precipitation_surface_current                                                                 (p, y, x) float32 ...\n",
       "    thompson_max_hail_k1_surface_current                                                                (p, y, x) float32 ...\n",
       "    thompson_max_hail_2d_surface_current                                                                (p, y, x) float32 ...\n",
       "    hailcast_max_hail_mean_surface_current                                                              (p, y, x) float32 ...\n",
       "    hailcast_max_hail_standard_deviation_surface_current                                                (p, y, x) float32 ...\n",
       "    vegetation_type_surface_current                                                                     (p, y, x) float32 ...\n",
       "    precipitable_water_entire_atmosphere_(considered_as_a_single_layer)_current                         (p, y, x) float32 ...\n",
       "    geopotential_height_cloud_base_current                                                              (p, y, x) float32 ...\n",
       "    geopotential_height_cloud_top_current                                                               (p, y, x) float32 ...\n",
       "    temperature_cloud_top_current                                                                       (p, y, x) float32 ...\n",
       "    pressure_reduced_to_msl_mean_sea_level_prev                                                         (p, y, x) float32 ...\n",
       "    composite_reflectivity_entire_atmosphere_prev                                                       (p, y, x) float32 ...\n",
       "    geopotential_height_500_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    temperature_500_mb_prev                                                                             (p, y, x) float32 ...\n",
       "    dew_point_temperature_500_mb_prev                                                                   (p, y, x) float32 ...\n",
       "    u-component_of_wind_500_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    v-component_of_wind_500_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    geopotential_height_700_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    temperature_700_mb_prev                                                                             (p, y, x) float32 ...\n",
       "    dew_point_temperature_700_mb_prev                                                                   (p, y, x) float32 ...\n",
       "    u-component_of_wind_700_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    v-component_of_wind_700_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    geopotential_height_850_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    temperature_850_mb_prev                                                                             (p, y, x) float32 ...\n",
       "    dew_point_temperature_850_mb_prev                                                                   (p, y, x) float32 ...\n",
       "    u-component_of_wind_850_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    v-component_of_wind_850_mb_prev                                                                     (p, y, x) float32 ...\n",
       "    reflectivity_4000_m_above_ground_prev                                                               (p, y, x) float32 ...\n",
       "    reflectivity_1000_m_above_ground_prev                                                               (p, y, x) float32 ...\n",
       "    temperature_2_m_above_ground_prev                                                                   (p, y, x) float32 ...\n",
       "    specific_humidity_2_m_above_ground_prev                                                             (p, y, x) float32 ...\n",
       "    dew_point_temperature_2_m_above_ground_prev                                                         (p, y, x) float32 ...\n",
       "    u-component_of_wind_10_m_above_ground_prev                                                          (p, y, x) float32 ...\n",
       "    v-component_of_wind_10_m_above_ground_prev                                                          (p, y, x) float32 ...\n",
       "    surface_lifted_index_500-1000_mb_prev                                                               (p, y, x) float32 ...\n",
       "    downward_short-wave_radiation_flux_surface_prev                                                     (p, y, x) float32 ...\n",
       "    storm_relative_helicity_3000-0_m_above_ground_prev                                                  (p, y, x) float32 ...\n",
       "    storm_relative_helicity_1000-0_m_above_ground_prev                                                  (p, y, x) float32 ...\n",
       "    u-component_storm_motion_0-6000_m_above_ground_prev                                                 (p, y, x) float32 ...\n",
       "    vertical_u-component_shear_0-6000_m_above_ground_prev                                               (p, y, x) float32 ...\n",
       "    vertical_v-component_shear_0-6000_m_above_ground_prev                                               (p, y, x) float32 ...\n",
       "    best_(4_layer)_lifted_index_180-0_mb_above_ground_prev                                              (p, y, x) float32 ...\n",
       "    convective_available_potential_energy_180-0_mb_above_ground_prev                                    (p, y, x) float32 ...\n",
       "    convective_inhibition_180-0_mb_above_ground_prev                                                    (p, y, x) float32 ...\n",
       "    geopotential_height_level_of_adiabatic_condensation_from_sfc_prev                                   (p, y, x) float32 ...\n",
       "    convective_available_potential_energy_255-0_mb_above_ground_prev                                    (p, y, x) float32 ...\n",
       "    convective_inhibition_255-0_mb_above_ground_prev                                                    (p, y, x) float32 ...\n",
       "    simulated_brightness_temperature_for_goes_12_channel_4_top_of_atmosphere_prev                       (p, y, x) float32 ...\n",
       "    simulated_brightness_temperature_for_goes_11_channel_3_top_of_atmosphere_prev                       (p, y, x) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        NCAR Ensemble Storm Patches for run 2016050300\n",
       "    institution:  National Center for Atmospheric Research"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(\"/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_03.nc\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_01.nc 640\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_02.nc 950\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_03.nc 760\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_04.nc 650\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_05.nc 605\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_06.nc 835\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_07.nc 760\n",
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      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016051200_mem_06.nc 1260\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016051200_mem_07.nc 1265\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016051200_mem_08.nc 1270\n",
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      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_04.nc 1840\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_05.nc 1780\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_06.nc 1520\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_07.nc 1990\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_08.nc 2110\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_09.nc 1850\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_10.nc 1745\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_01.nc 1980\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_02.nc 1910\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_03.nc 2250\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_04.nc 2410\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_05.nc 2190\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_06.nc 1785\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_07.nc 2250\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_08.nc 1775\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_09.nc 1915\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060200_mem_10.nc 1615\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_01.nc 1875\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_02.nc 1655\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_03.nc 1965\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_04.nc 1625\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_05.nc 1010\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_06.nc 1535\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_07.nc 1710\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_08.nc 1590\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_09.nc 1620\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_10.nc 1740\n"
     ]
    }
   ],
   "source": [
    "variables = [\"composite_reflectivity_entire_atmosphere_current\"]\n",
    "all_data, all_meta = load_storm_patch_data(data_path, variables, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "storm_norm_data, storm_scaling_values = normalize_multivariate_data(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_03.nc 760\n",
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016050300_mem_04.nc 650\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016051200_mem_06.nc 1260\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060100_mem_01.nc 2000\n",
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      "/users/dgagne/ncar_ens_storm_patches/ncar_ens_storm_patches_2016060300_mem_10.nc 1740\n"
     ]
    }
   ],
   "source": [
    "output_data, output_meta = load_storm_patch_data(data_path,\n",
    "                                                     [\"thompson_max_hail_k1_surface_current\",\n",
    "                                                      \"mask\"], 4)\n",
    "max_hail = np.array([output_data[i, :, :, 0][output_data[i, :, :, 1] > 0].max()\n",
    "                         for i in range(output_data.shape[0])])\n",
    "max_hail *= 1000\n",
    "hail_labels = np.where(max_hail >= 25, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_model(input_size=100, filter_width=5, min_data_width=4,\n",
    "                    min_conv_filters=64, output_size=(32, 32, 1), stride=2, activation=\"relu\",\n",
    "                    use_dropout=False, dropout_alpha=0,\n",
    "                    use_noise=False, noise_sd=0.1):\n",
    "    \"\"\"\n",
    "    Creates a generator convolutional neural network for a generative adversarial network set. The keyword arguments\n",
    "    allow aspects of the structure of the generator to be tuned for optimal performance.\n",
    "\n",
    "    Args:\n",
    "        input_size (int): Number of nodes in the input layer.\n",
    "        filter_width (int): Width of each convolutional filter\n",
    "        min_data_width (int): Width of the first convolved layer after the input layer\n",
    "        min_conv_filters (int): Number of convolutional filters in the last convolutional layer\n",
    "        output_size (tuple of size 3): Dimensions of the output\n",
    "        stride (int): Number of pixels that the convolution filter shifts between operations.\n",
    "        activation (str): Type of activation used for convolutional layers. Use \"leaky\" for Leaky ReLU.\n",
    "        output_activation (str): Type of activation used on the output layer\n",
    "        use_dropout (bool): Whether to use Dropout layers or not.\n",
    "        dropout_alpha (float): proportion of nodes dropped out.\n",
    "        use_noise: Whether to use a Gaussian noise layer after a convolution.\n",
    "        noise_sd: Standard deviation of the Gaussian noise.\n",
    "\n",
    "    Returns:\n",
    "        Model output graph, model input\n",
    "    \"\"\"\n",
    "    num_layers = int(np.log2(output_size[0]) - np.log2(min_data_width))\n",
    "    max_conv_filters = int(min_conv_filters * 2 ** (num_layers))\n",
    "    curr_conv_filters = max_conv_filters\n",
    "    vector_input = Input(shape=(input_size, ), name=\"gen_input\")\n",
    "    model = Dense(units=max_conv_filters * min_data_width * min_data_width, \n",
    "                  kernel_regularizer=l2(0.001), use_bias=False)(vector_input)\n",
    "    model = Reshape((min_data_width, min_data_width, max_conv_filters))(model)\n",
    "    if activation == \"leaky\":\n",
    "        model = LeakyReLU(alpha=0.1)(model)\n",
    "    else:\n",
    "        model = Activation(activation)(model)\n",
    "    for i in range(num_layers):\n",
    "        curr_conv_filters //= 2\n",
    "        model = Conv2D(curr_conv_filters, (filter_width, filter_width),\n",
    "                                strides=(stride, stride), padding=\"same\", kernel_regularizer=l2(0.001))(model)\n",
    "        if activation == \"leaky\":\n",
    "            model = LeakyReLU(alpha=0.1)(model)\n",
    "        else:\n",
    "            model = Activation(activation)(model)\n",
    "        if use_dropout:\n",
    "            model = Dropout(dropout_alpha)(model)\n",
    "        if use_noise:\n",
    "            model = GaussianNoise(noise_sd)(model)\n",
    "        if stride == 1:\n",
    "            model = UpSampling2D()(model)\n",
    "\n",
    "    model = Conv2D(curr_conv_filters, (filter_width, filter_width), \n",
    "                            padding=\"same\", kernel_regularizer=l2(0.001))(model)\n",
    "    if activation == \"leaky\":\n",
    "        model = LeakyReLU(alpha=0.1)(model)\n",
    "    else:\n",
    "        model = Activation(activation)(model)    \n",
    "    model = Conv2D(output_size[-1], (filter_width, filter_width),\n",
    "                            strides=(1, 1),\n",
    "                            padding=\"same\", kernel_regularizer=l2(0.001))(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model_out = Model(vector_input, model)\n",
    "    return model_out\n",
    "\n",
    "\n",
    "def encoder_model(input_size=(32, 32, 1), filter_width=5, min_data_width=4,\n",
    "                  min_conv_filters=64, output_size=100, stride=2, activation=\"relu\", output_activation=\"linear\",\n",
    "                  use_dropout=False, dropout_alpha=0, use_noise=False, noise_sd=0.1, pooling=\"mean\"):\n",
    "    \"\"\"\n",
    "    Creates an encoder convolutional neural network that reproduces the generator input vector. The keyword arguments\n",
    "    allow aspects of the structure of the generator to be tuned for optimal performance.\n",
    "\n",
    "    Args:\n",
    "        input_size (tuple of ints): Number of nodes in the input layer.\n",
    "        filter_width (int): Width of each convolutional filter\n",
    "        min_data_width (int): Width of the last convolved layer\n",
    "        min_conv_filters (int): Number of convolutional filters in the first convolutional layer\n",
    "        output_size (int): Dimensions of the output\n",
    "        stride (int): Number of pixels that the convolution filter shifts between operations.\n",
    "        activation (str): Type of activation used for convolutional layers. Use \"leaky\" for Leaky ReLU.\n",
    "        output_activation (str): Type of activation used on the output layer\n",
    "        use_dropout (bool): Whether to use Dropout layers or not.\n",
    "        dropout_alpha (float): proportion of nodes dropped out.\n",
    "        use_noise (bool): Whether to use a Gaussian noise layer after a convolution.\n",
    "        noise_sd (float): Standard deviation of the Gaussian noise.\n",
    "        pooling (str): Type of pooling to use if stride=1. Options: \"mean\" or \"max\".\n",
    "    Returns:\n",
    "        Keras convolutional neural network.\n",
    "    \"\"\"\n",
    "    num_layers = int(np.log2(input_size[0]) - np.log2(min_data_width))\n",
    "    curr_conv_filters = min_conv_filters\n",
    "    image_input = Input(shape=input_size, name=\"enc_input\")\n",
    "    model = None\n",
    "    for c in range(num_layers):\n",
    "        if c == 0:\n",
    "            model = Conv2D(curr_conv_filters, (filter_width, filter_width),\n",
    "                           strides=(stride, stride), padding=\"same\", kernel_regularizer=l2(0.001))(image_input)\n",
    "        else:\n",
    "            model = Conv2D(curr_conv_filters, (filter_width, filter_width),\n",
    "                           strides=(stride, stride), padding=\"same\", kernel_regularizer=l2(0.001))(model)\n",
    "        if activation == \"leaky\":\n",
    "            model = LeakyReLU(0.2)(model)\n",
    "        else:\n",
    "            model = Activation(activation)(model)\n",
    "        if use_dropout:\n",
    "            model = Dropout(dropout_alpha)(model)\n",
    "        if use_noise:\n",
    "            model = GaussianNoise(noise_sd)(model)\n",
    "        if stride == 1:\n",
    "            if pooling.lower() == \"mean\":\n",
    "                model = AveragePooling2D()(model)\n",
    "            else:\n",
    "                model = MaxPool2D()(model)\n",
    "        curr_conv_filters *= 2\n",
    "    model = Conv2D(curr_conv_filters, (filter_width, filter_width),\n",
    "                       strides=(1, 1), padding=\"same\", kernel_regularizer=l2(0.001))(model)\n",
    "    model = Flatten()(model)\n",
    "    model = Dense(output_size)(model)\n",
    "    model = Activation(output_activation)(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model_out = Model(image_input, model)\n",
    "    return model_out\n",
    "\n",
    "\n",
    "def discriminator_model(input_size=(32, 32, 1), stride=2, filter_width=5,\n",
    "                        min_conv_filters=64, min_data_width=4, activation=\"relu\",\n",
    "                        use_dropout=False, dropout_alpha=0, use_noise=False, noise_sd=0,\n",
    "                        pooling=\"mean\"):\n",
    "    \"\"\"\n",
    "    Creates an discriminator convolutional neural network that reproduces the generator input vector.\n",
    "    The keyword arguments allow aspects of the structure of the discriminator to be tuned for optimal performance.\n",
    "\n",
    "    Args:\n",
    "        input_size (tuple of ints): Number of nodes in the input layer.\n",
    "        filter_width (int): Width of each convolutional filter\n",
    "        min_data_width (int): Width of the last convolved layer\n",
    "        min_conv_filters (int): Number of convolutional filters in the first convolutional layer\n",
    "        stride (int): Number of pixels that the convolution filter shifts between operations.\n",
    "        activation (str): Type of activation used for convolutional layers. Use \"leaky\" for Leaky ReLU.\n",
    "        use_dropout (bool): Whether to use Dropout layers or not.\n",
    "        dropout_alpha (float): proportion of nodes dropped out.\n",
    "        use_noise (bool): Whether to use a Gaussian noise layer after a convolution.\n",
    "        noise_sd (float): Standard deviation of the Gaussian noise.\n",
    "        pooling (str): Type of pooling to use if stride=1. Options: \"mean\" or \"max\".\n",
    "\n",
    "    Returns:\n",
    "        discriminator model output, encoder model output, image input\n",
    "    \"\"\"\n",
    "    num_layers = int(np.log2(input_size[0]) - np.log2(min_data_width))\n",
    "    curr_conv_filters = min_conv_filters\n",
    "    image_input = Input(shape=input_size, name=\"enc_input\")\n",
    "    model = image_input\n",
    "    for c in range(num_layers):\n",
    "        model = Conv2DTranspose(curr_conv_filters, (filter_width, filter_width),\n",
    "                       strides=(stride, stride), padding=\"same\", kernel_regularizer=l2(0.001))(model)\n",
    "        if activation == \"leaky\":\n",
    "            model = LeakyReLU(0.2)(model)\n",
    "        else:\n",
    "            model = Activation(activation)(model)\n",
    "        if stride == 1:\n",
    "            if pooling.lower() == \"mean\":\n",
    "                model = AveragePooling2D()(model)\n",
    "            else:\n",
    "                model = MaxPool2D()(model)\n",
    "        if use_dropout:\n",
    "            model = Dropout(dropout_alpha)(model)\n",
    "        if use_noise:\n",
    "            model = GaussianNoise(noise_sd)(model)\n",
    "        curr_conv_filters *= 2\n",
    "    model = Conv2D(curr_conv_filters, (filter_width, filter_width),\n",
    "                       strides=(1, 1), padding=\"same\", kernel_regularizer=l2(0.001))(model)\n",
    "    model = Flatten()(model)\n",
    "    disc_model = Dense(1)(model)\n",
    "    disc_model = Activation(\"sigmoid\")(disc_model)\n",
    "    model_out = Model(image_input, disc_model)\n",
    "    return model_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = generator_model(min_conv_filters=16, min_data_width=8, stride=1, \n",
    "                      activation=\"leaky\", use_noise=True, noise_sd=0.01)\n",
    "disc = discriminator_model(min_conv_filters=16, min_data_width=8, stride=1, \n",
    "                           activation=\"leaky\", use_noise=True, noise_sd=0.01)\n",
    "enc = encoder_model(min_conv_filters=16, min_data_width=8, stride=1, \n",
    "                    activation=\"leaky\", use_noise=True, noise_sd=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Adam(lr=0.0001, beta_1=0.5, beta_2=0.9, amsgrad=False)\n",
    "gen.compile(optimizer=opt, loss=\"mse\")\n",
    "disc.compile(optimizer=opt, loss=\"binary_crossentropy\")\n",
    "enc.compile(optimizer=opt, loss=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stack_gen_disc(generator, discriminator):\n",
    "    \"\"\"\n",
    "    Combines generator and discrminator layers together while freezing the weights of the discriminator layers.\n",
    "\n",
    "    Args:\n",
    "        generator (Keras Model object): Generator model\n",
    "        discriminator (Keras Model object): Discriminator model\n",
    "\n",
    "    Returns:\n",
    "        Generator layers attached to discriminator layers.\n",
    "    \"\"\"\n",
    "    discriminator.trainable = False\n",
    "    model = discriminator(generator.output)\n",
    "    model_obj = Model(generator.input, model)\n",
    "    return model_obj\n",
    "\n",
    "\n",
    "def stack_gen_enc(generator, encoder):\n",
    "    \"\"\"\n",
    "    Combines generator and encoder layers together while freezing the weights of the generator layers.\n",
    "    This is used to train the encoder network to convert image data into a low-dimensional vector\n",
    "     representation.\n",
    "\n",
    "    Args:\n",
    "        generator: Decoder network\n",
    "        encoder: Encoder network\n",
    "    Returns:\n",
    "        Encoder layers attached to generator layers\n",
    "    \"\"\"\n",
    "    generator.trainable = False\n",
    "    model = encoder(generator.output)\n",
    "    model_obj = Model(generator.input, model)\n",
    "    return model_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_disc = stack_gen_disc(gen, disc)\n",
    "gen_disc.compile(optimizer=opt, loss=\"binary_crossentropy\")\n",
    "gen_enc = stack_gen_enc(gen, enc)\n",
    "gen_enc.compile(optimizer=opt, loss=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gen_input (InputLayer)       (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 8192)              827392    \n",
      "_________________________________________________________________\n",
      "reshape_2 (Reshape)          (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_5 (Conv2DTr (None, 8, 8, 64)          204864    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)   (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "gaussian_noise_7 (GaussianNo (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_6 (Conv2DTr (None, 16, 16, 32)        51232     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)   (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "gaussian_noise_8 (GaussianNo (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_7 (Conv2DTr (None, 32, 32, 32)        25632     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_12 (LeakyReLU)   (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_8 (Conv2DTr (None, 32, 32, 1)         801       \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 32, 32, 1)         4         \n",
      "_________________________________________________________________\n",
      "model_9 (Model)              (None, 1)                 265217    \n",
      "=================================================================\n",
      "Total params: 1,375,142\n",
      "Trainable params: 1,109,923\n",
      "Non-trainable params: 265,219\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gen_disc.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "enc_input (InputLayer)       (None, 32, 32, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        832       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_13 (LeakyReLU)   (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "average_pooling2d_5 (Average (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "gaussian_noise_9 (GaussianNo (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 16, 16, 64)        51264     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_14 (LeakyReLU)   (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "average_pooling2d_6 (Average (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "gaussian_noise_10 (GaussianN (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 128)         204928    \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 8193      \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 530,434\n",
      "Trainable params: 265,217\n",
      "Non-trainable params: 265,217\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/dgagne/miniconda3/envs/deep/lib/python3.6/site-packages/keras/engine/training.py:478: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    }
   ],
   "source": [
    "disc.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_gan_quiet(all_train_data, generator, discriminator, gen_disc, gen_enc, vec_size,\n",
    "                    batch_size, num_epochs, gan_index):\n",
    "    batch_size = int(batch_size)\n",
    "    batch_half = int(batch_size // 2)\n",
    "    batch_diff = all_train_data.shape[0] % batch_size\n",
    "    if batch_diff > 0:\n",
    "        train_data = all_train_data[:-batch_diff]\n",
    "    else:\n",
    "        train_data = all_train_data\n",
    "    train_order = np.arange(train_data.shape[0])\n",
    "    batch_labels = np.zeros(batch_size, dtype=np.float32)\n",
    "    batch_labels[:batch_half] = 1\n",
    "    gen_labels = np.ones(batch_size, dtype=np.float32)\n",
    "    batch_vec = np.zeros((batch_size, vec_size))\n",
    "    gen_batch_vec = np.zeros((batch_size, vec_size), dtype=train_data.dtype)\n",
    "    enc_batch_vec = np.zeros((batch_size, vec_size), dtype=train_data.dtype)\n",
    "    combo_data_batch = np.zeros(np.concatenate([[batch_size], train_data.shape[1:]]), dtype=np.float32)\n",
    "    disc_loss_history = []\n",
    "    gen_loss_history = []\n",
    "    for epoch in range(1, num_epochs + 1):\n",
    "        np.random.shuffle(train_order)\n",
    "        for b, b_index in enumerate(np.arange(batch_half, train_data.shape[0] + batch_half, batch_half)):\n",
    "            batch_vec[:] = np.random.normal(size=(batch_size, vec_size))\n",
    "            gen_batch_vec[:] = np.random.normal(size=(batch_size, vec_size))\n",
    "            combo_data_batch[:batch_half] = train_data[train_order[b_index - batch_half: b_index]]\n",
    "            combo_data_batch[batch_half:] = generator.predict_on_batch(batch_vec[batch_half:])\n",
    "            disc_loss_history.append(discriminator.train_on_batch(combo_data_batch, batch_labels))\n",
    "            disc_preds = discriminator.predict_on_batch(combo_data_batch)\n",
    "            gen_loss_history.append(gen_disc.train_on_batch(gen_batch_vec,\n",
    "                                                            gen_labels))\n",
    "            if b % 50 == 0:\n",
    "                print(\"Combo: {0} Epoch: {1} Batch: {2} Disc: {3:0.3f} Gen: {4:0.3f}\".format(gan_index,\n",
    "                                                                                             epoch, b,\n",
    "                                                                                             disc_loss_history[-1], \n",
    "                                                                                             gen_loss_history[-1]))\n",
    "    \n",
    "    gen_inputs = np.random.normal(size=(train_data.shape[0], vec_size))\n",
    "    print(\"Fit Encoder Combo: {0}\".format(gan_index))\n",
    "    #gen_enc.fit(gen_inputs, gen_inputs, epochs=num_epochs, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/dgagne/miniconda3/envs/deep/lib/python3.6/site-packages/keras/engine/training.py:478: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combo: 0 Epoch: 1 Batch: 0 Disc: 0.763 Gen: 1.021\n",
      "Combo: 0 Epoch: 1 Batch: 50 Disc: 0.729 Gen: 1.054\n",
      "Combo: 0 Epoch: 1 Batch: 100 Disc: 0.698 Gen: 1.120\n",
      "Combo: 0 Epoch: 1 Batch: 150 Disc: 0.803 Gen: 1.100\n",
      "Combo: 0 Epoch: 1 Batch: 200 Disc: 0.886 Gen: 0.927\n",
      "Combo: 0 Epoch: 1 Batch: 250 Disc: 0.711 Gen: 0.867\n",
      "Combo: 0 Epoch: 1 Batch: 300 Disc: 0.704 Gen: 0.969\n",
      "Combo: 0 Epoch: 1 Batch: 350 Disc: 0.713 Gen: 1.048\n",
      "Combo: 0 Epoch: 1 Batch: 400 Disc: 0.738 Gen: 1.053\n",
      "Combo: 0 Epoch: 2 Batch: 0 Disc: 0.741 Gen: 0.928\n",
      "Combo: 0 Epoch: 2 Batch: 50 Disc: 0.636 Gen: 0.923\n",
      "Combo: 0 Epoch: 2 Batch: 100 Disc: 0.584 Gen: 0.911\n",
      "Combo: 0 Epoch: 2 Batch: 150 Disc: 0.695 Gen: 1.011\n",
      "Combo: 0 Epoch: 2 Batch: 200 Disc: 0.601 Gen: 0.669\n",
      "Combo: 0 Epoch: 2 Batch: 250 Disc: 0.754 Gen: 1.015\n",
      "Combo: 0 Epoch: 2 Batch: 300 Disc: 0.800 Gen: 0.865\n",
      "Combo: 0 Epoch: 2 Batch: 350 Disc: 0.703 Gen: 0.929\n",
      "Combo: 0 Epoch: 2 Batch: 400 Disc: 0.730 Gen: 0.914\n",
      "Combo: 0 Epoch: 3 Batch: 0 Disc: 0.720 Gen: 0.704\n",
      "Combo: 0 Epoch: 3 Batch: 50 Disc: 0.774 Gen: 0.870\n",
      "Combo: 0 Epoch: 3 Batch: 100 Disc: 0.730 Gen: 0.778\n",
      "Combo: 0 Epoch: 3 Batch: 150 Disc: 0.778 Gen: 0.904\n",
      "Combo: 0 Epoch: 3 Batch: 200 Disc: 0.703 Gen: 0.797\n",
      "Combo: 0 Epoch: 3 Batch: 250 Disc: 0.728 Gen: 0.717\n",
      "Combo: 0 Epoch: 3 Batch: 300 Disc: 0.748 Gen: 0.901\n",
      "Combo: 0 Epoch: 3 Batch: 350 Disc: 0.736 Gen: 0.815\n",
      "Combo: 0 Epoch: 3 Batch: 400 Disc: 0.781 Gen: 0.828\n",
      "Combo: 0 Epoch: 4 Batch: 0 Disc: 0.738 Gen: 0.823\n",
      "Combo: 0 Epoch: 4 Batch: 50 Disc: 0.739 Gen: 0.810\n",
      "Combo: 0 Epoch: 4 Batch: 100 Disc: 0.741 Gen: 0.807\n",
      "Combo: 0 Epoch: 4 Batch: 150 Disc: 0.624 Gen: 0.758\n",
      "Combo: 0 Epoch: 4 Batch: 200 Disc: 0.744 Gen: 0.799\n",
      "Combo: 0 Epoch: 4 Batch: 250 Disc: 0.712 Gen: 0.694\n",
      "Combo: 0 Epoch: 4 Batch: 300 Disc: 0.722 Gen: 0.791\n",
      "Combo: 0 Epoch: 4 Batch: 350 Disc: 0.771 Gen: 0.785\n",
      "Combo: 0 Epoch: 4 Batch: 400 Disc: 0.701 Gen: 0.775\n",
      "Fit Encoder Combo: 0\n"
     ]
    }
   ],
   "source": [
    "train_gan_quiet(storm_norm_data, gen, disc, gen_disc, gen_enc, 100, 512, 4, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_codes = np.random.normal(size=(16, 100))\n",
    "gen_samples = gen.predict(random_codes)\n",
    "#enc_samples = enc.predict(gen_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_codes = enc.predict(storm_norm_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_train = gen.predict(train_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92.92037\n",
      "86.100975\n",
      "84.95398\n",
      "97.78012\n",
      "96.48079\n",
      "73.970634\n",
      "72.483246\n",
      "68.28801\n",
      "72.69274\n",
      "68.585976\n",
      "87.44389\n",
      "86.50556\n",
      "88.71172\n",
      "75.91026\n",
      "74.82147\n",
      "90.2031\n"
     ]
    }
   ],
   "source": [
    "train_neighbors = np.zeros(gen_samples.shape[0], dtype=int)\n",
    "for i in range(enc_samples.shape[0]):\n",
    "    train_neighbors[i] = np.argmin(np.sum((train_codes - enc_samples[i]) ** 2,axis=1))\n",
    "    print(np.sum((train_codes - enc_samples[i]) ** 2,axis=1).min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59709, 48569,  3130, 30628, 11253, 41896, 24159, 48473, 92966,\n",
       "       56212, 67397, 70130, 72186, 68141, 99144, 49933])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_hail_cases = max_hail.argsort()[::-1][0:16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, figsize=(16, 16), sharex=True, sharey=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "for a, ax in enumerate(axes.ravel()):\n",
    "    scaled_sample = gen_samples[a, :, :, 0] * storm_scaling_values.iloc[0, 1] + storm_scaling_values.iloc[0, 0]\n",
    "    pc = ax.pcolormesh(np.ma.array(scaled_sample, mask=scaled_sample <=10), \n",
    "                       vmin=-20, vmax=85, cmap=\"gist_ncar\")\n",
    "#plt.colorbar(pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x1152 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, figsize=(16, 16), sharex=True, sharey=True)\n",
    "plt.subplots_adjust(0.02, 0.02, 0.95, 0.95, wspace=0, hspace=0)\n",
    "for a, ax in enumerate(axes.ravel()):\n",
    "    scaled_sample = gen_train[big_hail_cases[a], :, :, 0] * storm_scaling_values.iloc[0, 1] + storm_scaling_values.iloc[0, 0]\n",
    "    pc = ax.pcolormesh(np.ma.array(scaled_sample, mask=scaled_sample <=10), \n",
    "                       vmin=-20, vmax=85, cmap=\"gist_ncar\")\n",
    "fig.suptitle(\"GAN Generated Big Hailstorms\", fontsize=18)\n",
    "plt.savefig(\"/users/dgagne/gan_generated_refl.png\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x1152 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, figsize=(16, 16), sharex=True, sharey=True)\n",
    "plt.subplots_adjust(0.02, 0.02, 0.95, 0.95, wspace=0, hspace=0)\n",
    "for a, ax in enumerate(axes.ravel()):\n",
    "    scaled_sample = storm_norm_data[big_hail_cases[a], :, :, 0] * storm_scaling_values.iloc[0, 1] + storm_scaling_values.iloc[0, 0]\n",
    "    pc = ax.pcolormesh(np.ma.array(scaled_sample, mask=scaled_sample <=10), \n",
    "                       vmin=-20, vmax=85, cmap=\"gist_ncar\")\n",
    "fig.suptitle(\"WRF Simulated Big Hailstorms\", fontsize=18)\n",
    "plt.savefig(\"/users/dgagne/wrf_generated_refl.png\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.609933"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_samples[a, :, :, 0].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59560, 67879, 67688, ..., 69451, 69453,     0])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit Encoder Combo: 0\n",
      "Epoch 1/12\n",
      " - 25s - loss: 1.4529\n",
      "Epoch 2/12\n",
      " - 23s - loss: 0.3639\n",
      "Epoch 3/12\n",
      " - 23s - loss: 0.2172\n",
      "Epoch 4/12\n",
      " - 23s - loss: 0.1776\n",
      "Epoch 5/12\n",
      " - 23s - loss: 0.1582\n",
      "Epoch 6/12\n",
      " - 23s - loss: 0.1459\n",
      "Epoch 7/12\n",
      " - 23s - loss: 0.1373\n",
      "Epoch 8/12\n",
      " - 23s - loss: 0.1307\n",
      "Epoch 9/12\n",
      " - 23s - loss: 0.1256\n",
      "Epoch 10/12\n",
      " - 23s - loss: 0.1214\n",
      "Epoch 11/12\n",
      " - 23s - loss: 0.1179\n",
      "Epoch 12/12\n",
      " - 23s - loss: 0.1148\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fbf24393780>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_inputs = np.random.normal(size=(storm_norm_data.shape[0] // 3, 100))\n",
    "print(\"Fit Encoder Combo: {0}\".format(0))\n",
    "gen_enc.fit(gen_inputs, gen_inputs, epochs=12, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_storm = gen.predict(enc.predict(storm_norm_data[0:1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fd20c36a780>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(storm_norm_data[0, :, :, 0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fd20c1ad1d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(gen_storm[0, :, :, 0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_storm"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
